Two-dimensional correlation analysis

Two dimensional correlation analysis is a mathematical technique that is used to study changes in measured signals.

In 2D correlation analysis, a sample is subjected to an external perturbation while all other parameters of the system are kept at the same value.

As a result of the controlled change (the perturbation), the system will undergo variations which are measured by a chemical or physical detection method.

The measured signals or spectra will show systematic variations that are processed with 2D correlation analysis for interpretation.

However, the interpretation of the measured signal becomes more tricky when spectra are complex and bands are heavily overlapping.

Two dimensional correlation analysis allows one to determine at which positions in such a measured signal there is a systematic change in a peak, either continuous rising or drop in intensity.

2D correlation analysis results in two complementary signals, which referred to as the 2D synchronous and 2D asynchronous spectrum.

2D correlation analysis is frequently used for its main advantage: increasing the spectral resolution by spreading overlapping peaks over two dimensions and as a result simplification of the interpretation of one-dimensional spectra that are otherwise visually indistinguishable from each other.

X- and y-axes are identical to the x-axis of the original dataset, whereas the different contours represent the magnitude of correlation between the spectral intensities.

Generally contour plots of 2D spectra are oriented with rising axes from left to right and top to down.

In some cases the Noda rules cannot be so readily implied, predominately when spectral features are not caused by simple intensity variations.